Unsupervised Text Segmentation Using Semantic Relatedness Graphs
Goran Glavaš, Federico Nanni, Simone Paolo Ponzetto · 2016
Segmenting text into semantically coherent fragments improves readability of text and facilitates tasks like text summarization and passage retrieval.In this paper, we present a novel unsupervised algorithm for linear text segmentation (TS) that exploits word embeddings and a measure of semantic relatedness of short texts to construct a semantic relatedness graph of the document.Semantically coherent segments are then derived from maximal cliques of the relatedness graph.The algorithm performs competitively on a standard synthetic dataset and outperforms the best-performing method on a real-world (i.e., non-artificial) dataset of political manifestos.